CN111024722A - Data fusion-based wood defect detection system and method - Google Patents
Data fusion-based wood defect detection system and method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/89—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles
- G01N21/892—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles characterised by the flaw, defect or object feature examined
- G01N21/898—Irregularities in textured or patterned surfaces, e.g. textiles, wood
- G01N21/8986—Wood
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/28—Measuring arrangements characterised by the use of optical techniques for measuring areas
- G01B11/285—Measuring arrangements characterised by the use of optical techniques for measuring areas using photoelectric detection means
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8854—Grading and classifying of flaws
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8854—Grading and classifying of flaws
- G01N2021/8874—Taking dimensions of defect into account
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30161—Wood; Lumber
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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Abstract
The invention discloses a wood defect detection system and method based on data fusion, which comprises the following steps: a rack; the conveying mechanism is arranged on the rack, and the wood board can be conveyed on the conveying mechanism; the conveying mechanism comprises a first station and a second station which are sequentially arranged in the conveying direction of the conveying mechanism; the linear array camera is arranged on the first station and used for acquiring images of the wood boards; and the linear structure optical sensor is arranged on the second station, collects point cloud data of the wood board, detects the wood board based on vision, extracts a defect part, selects the next processing line according to the defect area and type, and outputs parameters such as the defect type and area.
Description
Technical Field
The invention belongs to the technical field of furniture production, and particularly relates to a wood defect detection system and method based on data fusion.
Background
The composite floor of a furniture factory is composed of a plurality of boards with the thickness of 2mm, the boards may have the defects of dead knots, leaks, cracks and the like, the boards with certain specification defects need to be selected for subsequent treatment measures and then used, and the subsequent treatment measures of different defect types are different.
In the process of implementing the invention, the inventor finds that the prior art has at least the following problems: at present, the defective wood boards are manually sorted and then placed on a corresponding subsequent processing line, long-time sorting can cause fatigue of personnel or wrong sorting of the defective wood boards due to inertial thinking, and great quality problems can be brought to wood board synthesis.
Disclosure of Invention
The invention aims to provide a wood defect detection system and method based on data fusion, which are used for detecting a wood board based on vision, extracting a defect part, sorting the defect part to the next processing line according to the defect area and type and outputting parameters such as the defect type, the defect area and the like.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a data fusion-based wood defect detection system comprises:
a rack;
the conveying mechanism is arranged on the rack, and the wood board can be conveyed on the conveying mechanism;
the conveying mechanism comprises a first station and a second station, wherein the first station and the second station are sequentially arranged in the conveying direction of the conveying mechanism;
the linear array camera is arranged on the first station and used for acquiring images of the wood boards;
and the line structure light sensor is arranged on the second station and used for collecting point cloud data of the wood board.
The linear array camera and the line structure optical sensor are arranged on the supports.
The conveying mechanism is a conveying belt; and photoelectric sensors are arranged on two sides of the conveying belt on the rack.
The detection method of the wood defect detection system based on data fusion is characterized by comprising the following steps:
1) calibrating a conversion matrix H from a linear array camera pixel coordinate system to a linear structure optical sensor coordinate system;
2) detecting and identifying defects of a first station;
3) the second station distinguishes dead knot and leak defects;
4) and classifying and screening the defective wood boards.
The step 1) comprises the following steps:
1.1) respectively acquiring standard rectangular workpiece data by using a linear array camera and linear structure light, respectively extracting four corner points of the workpiece, and recording coordinates of the four corner points of p1, p2, p3 and p4 in a linear array camera pixel coordinate system, and coordinates of the four corner points of q1, q2, q3 and q4 in a linear structure light sensor coordinate system;
1.2) moving the standard rectangular workpiece, repeating the step (1.1), and recording p 5-p 8 and q 5-q 8;
1.3) solving a conversion matrix H from a linear array camera pixel coordinate system to a linear structure light sensor coordinate system by using least square fitting based on singular value decomposition.
The step 2) comprises the following steps:
2.1) after collecting the wood board picture, extracting the defect outline after carrying out graying, Gaussian filtering and local thresholding image preprocessing, and recording the center coordinate and the area size of each defect outline;
2.2) fitting a rotating rectangle to each defect contour, and determining the type of the crack defect according to the length-width ratio of the fitted rectangle, wherein the crack defect is formed when the length-width ratio is larger than a threshold thresh 1.
The step 3) comprises the following steps:
3.1) converting the central coordinate of the defect extracted from the first station under the pixel coordinate system to the linear structure light sensor coordinate system of the second station through an H matrix;
3.2) removing crack defect information selected at the first station;
3.3) calculating the density of the defect part position, wherein the density is larger than a threshold value thresh2 and is a dead junction defect, and is a leak defect.
And 4) classifying the wood board according to different defect types and conveying the wood board to a corresponding processing area for secondary processing.
One of the technical solutions has the following advantages or beneficial effects that the defect types can be quickly and accurately identified, information such as the size of the defect can be detected, the defect with larger size is discarded, the classification of the boards with different defect types can be completed, and the board with each defect type is sent to the corresponding processing area for secondary processing.
Drawings
FIG. 1 is a schematic structural diagram of a data fusion-based wood defect detection system provided in an embodiment of the present invention;
FIG. 2 is a control schematic diagram of the data fusion-based wood defect detection system of FIG. 1;
the labels in the above figures are: 1. the system comprises a rack, 2, a conveying mechanism, 3, a first station, 31, a linear array camera, 4, a second station, 41, a linear structure light sensor, 5 and a wood board.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Referring to fig. 1-2, a data fusion based wood defect detection system has:
a rack; the conveying mechanism is arranged on the rack, and the wood board can be conveyed on the conveying mechanism; the conveying mechanism comprises a first station and a second station which are sequentially arranged in the conveying direction of the conveying mechanism; the linear array camera is arranged on the first station and used for acquiring images of the wood boards; and the line structure light sensor is arranged on the second station and used for collecting point cloud data of the wood board.
All be equipped with the support on first station and the second station, linear array camera and line structure light sensor all install on the support. The conveying mechanism is a conveying belt; photoelectric sensors are arranged on the two sides of the conveying belt on the rack.
The thin wood boards are transmitted through the transmission mechanism, two stations are arranged above the conveyor belt, the industrial linear array camera is placed at the first station, the linear structure optical sensor is placed at the second station, and the transmission mechanism supports the wood boards to transmit through the two cross rods because the linear structure optical sensor has a height difference requirement on the collection of the leak defects. And two photoelectric sensors are arranged on the side of the conveyor belt, when a wood board is about to enter a linear array camera and linear structure light sensor scanning area, the photoelectric sensors sense that the wood board is in place, and send signals to trigger the linear array camera and linear structure light to carry out data acquisition, wherein a schematic diagram is shown in fig. 1.
Processing the image through image acquisition in a first station, extracting loopholes, dead knots and crack defects, screening out crack type defects through defect characteristic differences, and recording crack defect positions and sizes; simultaneously recording the central coordinates and the area of the loophole and the dead knot defect; and because the defects of holes and dead knots cannot be distinguished, the types of the holes and the dead knots are distinguished according to the density through the point cloud data collected by the second station.
The detection method of the wood defect detection system based on data fusion comprises the following steps:
1. calibrating a conversion matrix H from a linear array camera pixel coordinate system to a linear structure optical sensor coordinate system:
(1) respectively acquiring standard rectangular workpiece data by using a linear array camera and linear structure light, respectively extracting four corner points of the workpiece, and recording coordinates of the four corner points of p1, p2, p3 and p4 under a linear array camera pixel coordinate system, and coordinates of the four corner points of q1, q2, q3 and q4 under a linear structure light sensor coordinate system;
(2) moving the standard rectangular workpiece, repeating the step (1), and recording p 5-p 8 and q 5-q 8;
(3) solving a conversion matrix H from a linear array camera pixel coordinate system to a linear structure optical sensor coordinate system by using least square fitting based on singular value decomposition;
2. detecting and identifying defects of a first station:
(1) after collecting a wood board picture, extracting a defect outline after image preprocessing such as graying, Gaussian filtering, local thresholding and the like, and recording the central coordinate and the area size of each defect outline;
(2) the crack defect is in a thin strip shape, and the dead knot and the leak hole are in a similar round shape; and fitting a rotating rectangle to each defect contour, and determining the type of the crack defect according to the length-width ratio of the fitted rectangle, wherein the crack defect with the length-width ratio larger than a threshold thresh1 is the crack defect, and 10 is taken by thresh1 according to an empirical value.
3. And distinguishing dead knot and leak defects by the second station:
(1) converting the central coordinate of the defect extracted from the first station under the pixel coordinate system into the coordinate system of the line-structured light sensor of the second station through an H matrix;
(2) removing crack defect information picked out at the first station;
(3) because the board at the leak defect part is hollow, and the board at the dead knot defect part is solid, the densities of the two defects in point cloud data acquired by linear structured light are different, and the density is greater than a threshold value thresh2 to be a dead knot defect through density calculation of the defect part position, otherwise to be a leak defect, wherein thresh2 is 1000 according to an empirical value.
4. Classifying and screening defective wood boards:
and discarding the wood boards with larger defect sizes, classifying the rest wood boards according to different defect types, and sending the wood boards to the corresponding processing areas for secondary processing.
The defect types can be quickly and accurately identified, information such as defect sizes can be detected, defects with larger sizes are discarded, classification of boards with different defect types can be completed, and the boards with each defect type are sent to the corresponding processing area to be processed for the second time.
The invention has been described above with reference to the accompanying drawings, it is obvious that the invention is not limited to the specific implementation in the above-described manner, and it is within the scope of the invention to apply the inventive concept and solution to other applications without substantial modification.
Claims (8)
1. A wood defect detection system based on data fusion is characterized by comprising:
a rack;
the conveying mechanism is arranged on the rack, and the wood board can be conveyed on the conveying mechanism;
the conveying mechanism comprises a first station and a second station, wherein the first station and the second station are sequentially arranged in the conveying direction of the conveying mechanism;
the linear array camera is arranged on the first station and used for acquiring images of the wood boards;
and the line structure light sensor is arranged on the second station and used for collecting point cloud data of the wood board.
2. The data fusion-based wood defect detection system of claim 1, wherein a support is provided on each of the first and second stations, and the line-scan camera and the line-structured light sensor are mounted on the supports.
3. The data fusion-based wood defect detection system of claim 2, wherein the conveying mechanism is a conveyor belt; and photoelectric sensors are arranged on two sides of the conveying belt on the rack.
4. A method for detecting a wood defect detecting system based on data fusion according to any one of claims 1-3, characterized by comprising the following steps:
1) calibrating a conversion matrix H from a linear array camera pixel coordinate system to a linear structure optical sensor coordinate system;
2) detecting and identifying defects of a first station;
3) the second station distinguishes dead knot and leak defects;
4) and classifying and screening the defective wood boards.
5. The method for detecting a wood defect detecting system based on data fusion according to claim 4, wherein the step 1) comprises the steps of:
1.1) respectively acquiring standard rectangular workpiece data by using a linear array camera and linear structure light, respectively extracting four corner points of the workpiece, and recording coordinates of the four corner points as p1, p2, p3 and p4 under a pixel coordinate system of the linear array camera, and coordinates of the four corner points as q1, q2, q3 and q4 under a coordinate system of a linear structure light sensor;
1.2) moving the standard rectangular workpiece, repeating the step (1.1), and recording p 5-p 8 and q 5-q 8;
1.3) solving a conversion matrix H from a linear array camera pixel coordinate system to a linear structure light sensor coordinate system by using least square fitting based on singular value decomposition.
6. The method for detecting a wood defect detecting system based on data fusion according to claim 5, wherein the step 2) comprises the steps of:
2.1) after collecting the wood board picture, extracting the defect outline after carrying out graying, Gaussian filtering and local thresholding image preprocessing, and recording the center coordinate and the area size of each defect outline;
2.2) fitting a rotating rectangle to each defect contour, and determining the type of the crack defect according to the length-width ratio of the fitted rectangle, wherein the crack defect is formed when the length-width ratio is larger than a threshold thresh 1.
7. The method for detecting a wood defect detecting system based on data fusion according to claim 5, wherein the step 3) comprises the steps of:
3.1) converting the central coordinate of the defect extracted from the first station under the pixel coordinate system to the linear structure light sensor coordinate system of the second station through an H matrix;
3.2) removing crack defect information selected at the first station;
3.3) calculating the density of the defect part position, wherein the density is larger than a threshold value thresh2 and is a dead junction defect, and is a leak defect.
8. The method as claimed in claim 5, wherein in step 4), the wood board is classified according to the type of the wood board, and is sent to the corresponding processing area for secondary processing.
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Cited By (3)
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| CN112595266A (en) * | 2020-12-02 | 2021-04-02 | 武汉中仪物联技术股份有限公司 | Defect area calculation method and system for pipeline detection |
| CN113141901A (en) * | 2021-01-29 | 2021-07-23 | 山东汇友市政园林集团有限公司 | Split type arbor and shrub remote acceptance trimmer and acceptance method |
| CN117090133A (en) * | 2023-08-23 | 2023-11-21 | 青岛迪乐普精密机械有限公司 | Guardrail and detection method thereof |
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| CN113141901A (en) * | 2021-01-29 | 2021-07-23 | 山东汇友市政园林集团有限公司 | Split type arbor and shrub remote acceptance trimmer and acceptance method |
| CN117090133A (en) * | 2023-08-23 | 2023-11-21 | 青岛迪乐普精密机械有限公司 | Guardrail and detection method thereof |
| CN117090133B (en) * | 2023-08-23 | 2024-06-11 | 青岛迪乐普精密机械有限公司 | Guardrail and detection method thereof |
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